Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations270
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory108.5 B

Variable types

Numeric5
Categorical9

Alerts

HDisease is highly overall correlated with ThalliumHigh correlation
Thallium is highly overall correlated with HDiseaseHigh correlation
Stdep has 85 (31.5%) zeros Zeros

Reproduction

Analysis started2025-01-28 13:56:28.744455
Analysis finished2025-01-28 13:56:53.522601
Duration24.78 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct41
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.433333
Minimum29
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-01-28T19:26:53.648416image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile40
Q148
median55
Q361
95-th percentile68
Maximum77
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1090665
Coefficient of variation (CV)0.16734354
Kurtosis-0.54481539
Mean54.433333
Median Absolute Deviation (MAD)7
Skewness-0.16361523
Sum14697
Variance82.975093
MonotonicityNot monotonic
2025-01-28T19:26:53.934940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
54 16
 
5.9%
58 15
 
5.6%
51 12
 
4.4%
57 12
 
4.4%
59 12
 
4.4%
60 12
 
4.4%
52 11
 
4.1%
62 11
 
4.1%
44 10
 
3.7%
41 9
 
3.3%
Other values (31) 150
55.6%
ValueCountFrequency (%)
29 1
 
0.4%
34 2
 
0.7%
35 3
 
1.1%
37 2
 
0.7%
38 1
 
0.4%
39 3
 
1.1%
40 3
 
1.1%
41 9
3.3%
42 8
3.0%
43 7
2.6%
ValueCountFrequency (%)
77 1
 
0.4%
76 1
 
0.4%
74 1
 
0.4%
71 3
 
1.1%
70 4
1.5%
69 3
 
1.1%
68 3
 
1.1%
67 8
3.0%
66 6
2.2%
65 8
3.0%

sex
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
1
183 
0
87 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

Length

2025-01-28T19:26:54.119390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:54.264160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

Most occurring characters

ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 183
67.8%
0 87
32.2%

chpain
Categorical

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
4
129 
3
79 
2
42 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row2
4th row4
5th row2

Common Values

ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

Length

2025-01-28T19:26:54.413890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:54.571248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

Most occurring characters

ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 129
47.8%
3 79
29.3%
2 42
 
15.6%
1 20
 
7.4%

bp
Real number (ℝ)

Distinct47
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.34444
Minimum94
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-01-28T19:26:54.777985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum94
5-th percentile106.9
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range106
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.861608
Coefficient of variation (CV)0.13599059
Kurtosis0.92309674
Mean131.34444
Median Absolute Deviation (MAD)10
Skewness0.72261801
Sum35463
Variance319.03705
MonotonicityNot monotonic
2025-01-28T19:26:54.992482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
120 34
 
12.6%
130 31
 
11.5%
140 30
 
11.1%
110 17
 
6.3%
150 17
 
6.3%
160 11
 
4.1%
125 10
 
3.7%
138 9
 
3.3%
112 9
 
3.3%
128 9
 
3.3%
Other values (37) 93
34.4%
ValueCountFrequency (%)
94 2
 
0.7%
100 4
 
1.5%
101 1
 
0.4%
102 2
 
0.7%
104 1
 
0.4%
105 3
 
1.1%
106 1
 
0.4%
108 6
 
2.2%
110 17
6.3%
112 9
3.3%
ValueCountFrequency (%)
200 1
 
0.4%
192 1
 
0.4%
180 3
 
1.1%
178 2
 
0.7%
174 1
 
0.4%
172 1
 
0.4%
170 2
 
0.7%
165 1
 
0.4%
160 11
4.1%
158 1
 
0.4%

chol
Real number (ℝ)

Distinct144
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.65926
Minimum126
Maximum564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-01-28T19:26:55.190775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile177
Q1213
median245
Q3280
95-th percentile326.55
Maximum564
Range438
Interquartile range (IQR)67

Descriptive statistics

Standard deviation51.686237
Coefficient of variation (CV)0.20702712
Kurtosis4.895599
Mean249.65926
Median Absolute Deviation (MAD)32.5
Skewness1.1837209
Sum67408
Variance2671.4671
MonotonicityNot monotonic
2025-01-28T19:26:55.392497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234 6
 
2.2%
269 5
 
1.9%
254 5
 
1.9%
212 4
 
1.5%
243 4
 
1.5%
211 4
 
1.5%
233 4
 
1.5%
197 4
 
1.5%
282 4
 
1.5%
226 4
 
1.5%
Other values (134) 226
83.7%
ValueCountFrequency (%)
126 1
0.4%
141 1
0.4%
149 2
0.7%
160 1
0.4%
164 1
0.4%
166 1
0.4%
167 1
0.4%
168 1
0.4%
172 1
0.4%
174 1
0.4%
ValueCountFrequency (%)
564 1
0.4%
417 1
0.4%
409 1
0.4%
407 1
0.4%
394 1
0.4%
360 1
0.4%
354 1
0.4%
353 1
0.4%
341 1
0.4%
340 1
0.4%

FBS
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
230 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

Length

2025-01-28T19:26:55.562449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:55.710657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230
85.2%
1 40
 
14.8%

EKG
Categorical

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2
137 
0
131 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

Length

2025-01-28T19:26:55.876722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:56.018387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 137
50.7%
0 131
48.5%
1 2
 
0.7%

HR
Real number (ℝ)

Distinct90
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.67778
Minimum71
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-01-28T19:26:56.201087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile108
Q1133
median153.5
Q3166
95-th percentile182
Maximum202
Range131
Interquartile range (IQR)33

Descriptive statistics

Standard deviation23.165717
Coefficient of variation (CV)0.15477058
Kurtosis-0.10307189
Mean149.67778
Median Absolute Deviation (MAD)15.5
Skewness-0.52773668
Sum40413
Variance536.65043
MonotonicityNot monotonic
2025-01-28T19:26:56.451538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162 10
 
3.7%
160 9
 
3.3%
163 8
 
3.0%
172 7
 
2.6%
125 7
 
2.6%
152 6
 
2.2%
173 6
 
2.2%
142 6
 
2.2%
158 6
 
2.2%
132 6
 
2.2%
Other values (80) 199
73.7%
ValueCountFrequency (%)
71 1
 
0.4%
88 1
 
0.4%
95 1
 
0.4%
96 2
0.7%
97 1
 
0.4%
99 1
 
0.4%
103 2
0.7%
105 3
1.1%
106 1
 
0.4%
108 2
0.7%
ValueCountFrequency (%)
202 1
0.4%
195 1
0.4%
194 1
0.4%
192 1
0.4%
190 1
0.4%
188 1
0.4%
187 1
0.4%
186 2
0.7%
185 1
0.4%
184 1
0.4%

Exang
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
181 
1
89 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Length

2025-01-28T19:26:56.654417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:56.796351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Most occurring characters

ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 181
67.0%
1 89
33.0%

Stdep
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.05
Minimum0
Maximum6.2
Zeros85
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2025-01-28T19:26:56.961077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.8
Q31.6
95-th percentile3.31
Maximum6.2
Range6.2
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1452098
Coefficient of variation (CV)1.090676
Kurtosis1.7593165
Mean1.05
Median Absolute Deviation (MAD)0.8
Skewness1.2628932
Sum283.5
Variance1.3115056
MonotonicityNot monotonic
2025-01-28T19:26:57.213790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 85
31.5%
1.2 14
 
5.2%
1.4 13
 
4.8%
0.6 12
 
4.4%
1 12
 
4.4%
1.6 11
 
4.1%
0.8 11
 
4.1%
0.2 11
 
4.1%
1.8 10
 
3.7%
2 8
 
3.0%
Other values (29) 83
30.7%
ValueCountFrequency (%)
0 85
31.5%
0.1 6
 
2.2%
0.2 11
 
4.1%
0.3 3
 
1.1%
0.4 8
 
3.0%
0.5 5
 
1.9%
0.6 12
 
4.4%
0.7 1
 
0.4%
0.8 11
 
4.1%
0.9 3
 
1.1%
ValueCountFrequency (%)
6.2 1
 
0.4%
5.6 1
 
0.4%
4.2 2
0.7%
4 2
0.7%
3.8 1
 
0.4%
3.6 4
1.5%
3.5 1
 
0.4%
3.4 2
0.7%
3.2 2
0.7%
3.1 1
 
0.4%

sloSt
Categorical

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
1
130 
2
122 
3
18 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Length

2025-01-28T19:26:57.471006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:57.618665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Most occurring characters

ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 130
48.1%
2 122
45.2%
3 18
 
6.7%

Vesfluro
Categorical

Distinct4
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
160 
1
58 
2
33 
3
19 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Length

2025-01-28T19:26:57.820924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:58.052378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
59.3%
1 58
 
21.5%
2 33
 
12.2%
3 19
 
7.0%

Thallium
Categorical

High correlation 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
3
152 
7
104 
6
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row7
3rd row7
4th row7
5th row3

Common Values

ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

Length

2025-01-28T19:26:58.338934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:58.566100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

Most occurring characters

ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 152
56.3%
7 104
38.5%
6 14
 
5.2%

HDisease
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
0
150 
1
120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters270
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Length

2025-01-28T19:26:58.778747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T19:26:59.034846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Most occurring characters

ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common 270
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
55.6%
1 120
44.4%

Interactions

2025-01-28T19:26:52.288043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:34.585567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:49.968764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.591964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:51.575305image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:52.420646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:49.464319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.099586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.693252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:51.712745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:52.581199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:49.594307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.220128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.804388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:51.878350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:52.703376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:49.699098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.331343image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.899307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:51.995517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:52.849352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:49.823225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:50.462658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:51.025898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2025-01-28T19:26:52.144045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2025-01-28T19:26:59.252628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
EKGExangFBSHDiseaseHRStdepThalliumVesfluroagebpcholchpainsexsloSt
EKG1.0000.0510.0000.1610.1730.1420.0000.0000.2740.2640.0880.0910.0990.101
Exang0.0511.0000.0000.4080.3650.2470.3100.1820.0640.0890.0000.4240.1610.269
FBS0.0000.0001.0000.0000.0000.0000.0000.0660.0000.0780.0000.1220.0000.122
HDisease0.1610.4080.0001.0000.3940.4120.5190.4720.2570.0330.1290.4940.2840.378
HR0.1730.3650.0000.3941.000-0.4230.2030.173-0.400-0.043-0.0560.1840.1990.294
Stdep0.1420.2470.0000.412-0.4231.0000.2710.1780.2580.1830.0140.1600.0570.448
Thallium0.0000.3100.0000.5190.2030.2711.0000.1580.1420.0000.0000.2210.3950.213
Vesfluro0.0000.1820.0660.4720.1730.1780.1581.0000.2060.0410.0580.1420.1130.089
age0.2740.0640.0000.257-0.4000.2580.1420.2061.0000.2770.2110.1000.0000.000
bp0.2640.0890.0780.033-0.0430.1830.0000.0410.2771.0000.1900.1050.0000.076
chol0.0880.0000.0000.129-0.0560.0140.0000.0580.2110.1901.0000.0000.2390.000
chpain0.0910.4240.1220.4940.1840.1600.2210.1420.1000.1050.0001.0000.1060.177
sex0.0990.1610.0000.2840.1990.0570.3950.1130.0000.0000.2390.1061.0000.000
sloSt0.1010.2690.1220.3780.2940.4480.2130.0890.0000.0760.0000.1770.0001.000

Missing values

2025-01-28T19:26:53.091518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-28T19:26:53.407513image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agesexchpainbpcholFBSEKGHRExangStdepsloStVesfluroThalliumHDisease
070141303220210902.42331
167031155640216001.62070
257121242610014100.31071
364141282630010510.22170
474021202690212110.21130
565141201770014000.41070
656131302561214210.62161
759141102390214211.22171
860141402930217001.22271
963041504070215404.02371
agesexchpainbpcholFBSEKGHRExangStdepsloStVesfluroThalliumHDisease
26058031203400017200.01030
26160141302060213212.42271
26258121202840216001.82031
26349121302660017100.61030
26448121102290016801.03071
26552131721991016200.51070
26644121202630017300.01070
26756021402940215301.32030
26857141401920014800.42060
26967141602860210811.52331